scholarly journals Inference of maternal allele inheritance via Bayesian hierarchical model in noninvasive prenatal diagnosis

2016 ◽  
Author(s):  
Chunxuan Shao
Keyword(s):  
Prenatal Diagnosis ◽  
Hierarchical Model ◽  
Simulated Data ◽  
Haplotype Frequency ◽  
Bayesian Hierarchical Model ◽  
Maternal Plasma ◽  
Genetic Alterations ◽  
Maternal Allele ◽  
Bayesian Hierarchical ◽  
Noninvasive Prenatal Diagnosis

AbstractNoninvasive prenatal diagnosis (NIPD) poses a promising solution for detecting genetic alterations in fetus genome. However, the inference of the maternal allele inheritance in monogenic autosomal recessive disease is still challenging. Here the Bayesian hierarchical model is proposed to deduce the allele inheritance basing on haplotype frequency. The Bayesian approach, which does not depend on the knowledge of fetus DNA proportion in maternal plasma, provides accurate estimations on both real and simulated data; moreover, it is most robust than current methods in analyzing noisy or even erroneous data.

scholarly journals A Bayesian hierarchical model of lexicographic choice

2017 ◽  
Author(s):  
◽  
Sanghyuk Park
Keyword(s):  
Hierarchical Model ◽  
Simulated Data ◽  
Bayesian Hierarchical Model ◽  
Decision Makers ◽  
Choice Data ◽  
Decision Making Under Risk ◽  
Bayesian Hierarchical ◽  
Parameter Recovery ◽  
Variable Threshold ◽  
Frame Work

I present a lexicographic, threshold-based model of choice used to evaluate decision makers' preferences among risky alternatives. Using a hierarchical Bayesian frame-work, this model is able to account for observed individual differences by allowing for variable threshold values in attribute features, as well as the order that individuals consider attributes of the choice alternatives. Performance of the model is evaluated via a parameter recovery test using simulated data. I also apply the model to the choice data from a decision-making-under-risk experiment (Davis-Stober, Brown and Cavagnaro, 2015). Bayesian p-values are obtained to check the model fits for every individual, and sensitivity analysis is carried out to measure the degree to which choices of prior distributions affect the results. Finally, I discuss the implications of the Bayesian hierarchical model of lexicographic choice I present in this paper.

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

2019 ◽  
Vol 15 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin Ingram
Keyword(s):  
Random Walk ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Gaussian Random Walk ◽  
Over Time ◽  
Modelling Approach ◽  
Independent And Identically Distributed

Abstract A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

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